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Dr. Hagan has taught and conducted research in the areas of machine learning, statistical modeling and control systems for the last forty years. His research has encompassed a variety of application areas: drug discovery, molecular dynamics, seismic signal processing, genetic pathway modeling, optimal portfolio management, electric load prediction, flight simulators, precision pointing systems, diesel engines, adaptive flight control and friction compensation. He has received grants from Boeing, Texas Instruments, Halliburton Energy Services, Cummins Engine Company, National Science Foundation, Air Force Office of Scientific Research, California Public Employees Retirement System (CalPERS), Amgen, and FlightSafety International. For the last thirty years his research has focused on the use of neural networks for classification, prediction, nonlinear filtering and control. He is author, with Howard Demuth and Mark Beale, of a textbook, Neural Network Design, which has been translated into Chinese, Korean and Farsi. He is also a co-author of the Neural Network Toolbox for MATLAB. He has given keynote addresses on neural networks at a variety of international conferences. He was a visiting scholar during 1994 at the University of Canterbury in Christchurch, New Zealand and during 2005-2006 at the Laboratoire d'Analyse et d'Architecture des Systèms du Centre National de la Recherche Scientifique in Toulouse, France. He has taught courses in neural networks, stochastic processes, estimation theory, system identification and control systems. He was awarded the Oklahoma State University Regents Distinguished Teaching Award in 2000 and the Lockheed Martin Aeronautics Teaching Excellence Award in 2005 and 2010.
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EXPERT SYSTEMS WITH APPLICATIONSno. Part A (2024): 121437-121437
IEEE/ASME Transactions on Mechatronicsno. 3 (2020): 1499-1509
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